Is Your Supply Chain Organized for Demand Forecasting and Planning in a Modern e-Commerce, Data-Driven Supply Chain?

Most demand planners and managers operate with a flat-earth mindset that suggests a forecast is “just a number” without embracing uncertainty as a quantifiable factor. Moreover, they often fail to recognize the paradigm shift and the proper role of a consumer data-driven forecaster in a balanced sales & operations plan.

What are some of the best-in-class processes for achieving an agile and sustainable performance in an e-commerce forecasting environment? There are opportunities for making quantitatively-challenging processes more accessible and useful to supply chain practitioners.?In my book Change&Chance Embraced: Achieving Agility with Smarter Forecasting in the Supply Chain, I describe a four-stage Agile Forecasting process involving (1) ‘big data’ issues of data quality, outlier resistance, data exploration and predictive visualization, and database forecast decision support (FDSP), (2) predictive analytics suitable for automating useful demand forecast modeling solutions, (3) an objective approach to forecast accuracy measurement and (4) challenges of reconciling statistical models and forecasts to support a synchronized, integrated business planning (IBP) process.?

In a traditional push paradigm (Sell What You Can Make) supply chain, forecasting tends to follow, rather than lead, someone’s business plan. In a 21st century e-commerce pull paradigm (Make What You Can Sell) supply chain, demand forecasting should become independent of demand and operations planning.

In the context of an external data-driven ?PULL paradigm supply chain, a demand planner is in the business of making detailed decisions around the future demand for products and services in the face of uncertainty. For instance, demand forecasting and planning is the process that drives inventory levels to improve a company’s ability to replenish or fulfill product to meet customer (and ultimate consumer) needs in a timely and cost-effective way. If forecasting does not have a good link to drive inventory stocks, improving it won’t necessarily improve customer service levels or reduce costs.

Business planners and managers use detailed item-level (disaggregated) by product/location/period) forecast data from a number of sources to create a clear view of what product demand is likely to be, and then link inventory and replenishment processes to that future view. This bottom up demand forecast incorporates a logical and coherent series of steps that, if performed in a consistent, management-supported fashion, can improve forecasting agility, reliability and credibility throughout the supply chain. (Visual courtesy: Simon Conradie)

Demand managers must then reconcile their planning approaches with the assumptions for the future so that the most credible methodology will produce accurate and reliable forecasts. The Demand Manager (DM) makes use of digital computerized intelligence to synchronize and optimize essential elements of manufacture and distribution

One of the?five-star customer reviews of Change&Chance Embrtaced on Amazon.com reads:

You don't need an MBA or a degree in mathematics to get significant value out of this book.?

Charles ReCorr (August 26, 2018) writes: This book is an easy read for what most people believe is a difficult subject. It's not written to impress you, but rather to educate you to make better, more useful, forecasts supported by data not simply opinions. It is extremely functional and well written. If your job description includes forecasting, or you want to learn how to forecast, this is a must have.

In that business forecasting book, a demand forecast is not regarded as just a number, outcome or task. It is part of an ongoing process directly affecting sales, marketing, inventory, production and all other aspects of the modern supply chain. In the supply chain, demand planners and managers should start by building on an independently derived, unbiased baseline demand forecast, rather than relying primarily on the targets that planners hope will result from a sales forecast.

In this book, the introductory chapter describes?

• ?what a demand forecasting process is
• ?why it is a necessary discipline for demand planners and managers to become familiar with
• ?a new role for demand managers in a modern, consumer data-driven supply chain
• ·how, when, where, and by whom the demand forecasting job is done
• ·the systematic steps for an agile execution of the demand forecasting process in the supply chain